Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection

Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection

Feature selection aims to eliminate redundant or irrelevant variables from input data to reduce computational cost, provide a better understanding of data and improve prediction accuracy. Majority of the existing filter methods utilize a single feature-ranking technique, which may overlook some impo...

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Bibliographic Details
Main Authors: Anwar Ul Haq, Defu Zhang, He Peng, Sami Ur Rahman
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Classification
clustering of variables
feature selection
filter methods
random forests
Online Access:https://ieeexplore.ieee.org/document/8871132/

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